The start-up gap and jobs

Abstract

The present paper uses quarterly data from the social security registry covering the full population of Belgian firms to analyze how the secular decline in the firm entry rate affects aggregate employment. To this end, we disentangle the entry margin into two channels: the overall employment of new firms (the start-up employment) and the share of start-up employment by sector (the sectoral composition of start-ups). We find that the decline in start-up employment slowed down the growth rate of aggregate employment by 26% over the 2009Q2–2017Q1 period by shifting the age distribution of firms toward older firms. The sectoral composition of start-ups accelerated the decline in the manufacturing sector and prevented the distribution sector from a potential decline, while leaving the aggregate employment unchanged.

Appendix

1.1 Understanding the rise of services and its heterogeneity

Differentiating the distribution sector from the services sector highlighted the differences in employment dynamics between the two sectors. Young firms in services have higher survival and conditional employment growth rates than young firms in distribution. Within services, we can further differentiate between more and less knowledge intensive firms. We use average firm wages as a proxy for knowledge intensity. Using this proxy suggests that firms which pay higher average wages are more knowledge intensive.

We split the services sector into two segments, high and low knowledge intensity segments. The NSSO data include total gross wage per firm per quarter. We compute average gross wages per FTE per quarter by dividing total gross wages by FTE number of employees.

Table 6 reports summary statistics of the data on services by knowledge intensity.

Table 6 The table reports summary statistics of NSSO data on services by knowledge intensity for 2008Q1–2017Q1 period

High-knowledge intensive services include information and communication activities, financial and insurance activities, and professional and technical activities. Low-knowledge intensive sectors include administrative and support activities. While there are more high-knowledge intensive firms, on average they are smaller in size than low-knowledge intensive firms. On the other hand, high-knowledge intensive firms pay 30% higher wages than their low-knowledge intensive counterparts

Figure 9 plots the evolution of sectoral composition of services by knowledge intensity. The manufacturing and distribution sectors are not shown for convenience. The solid line plots the evolution of employment share of high- and low-knowledge intensity services. While the employment share of high-knowledge intensity services remained stable around 20%, the employment share of low-knowledge intensity services has increased from 14 to 18% of the economy during the 2008–2017 period. The dashed line plots the share of start-up employment by knowledge intensity of services. There is more entry in high-knowledge services with 24% of start-up employment than in low-knowledge intensity services with 12% of start-up employment.

Fig. 9

The figure plots the evolution of sectoral composition of services by knowledge intensity. The solid line plots the employment share of services by knowledge intensity. The dashed line plots the share of start-up employment by knowledge intensity. The manufacturing and distribution sectors are not shown in the figure

Table 7 reports the mean values of services’ age-specific survival and conditional growth rates by knowledge intensity. The survival rate of young high-knowledge intensity services is higher than the survival rate of young low-knowledge intensity services. The conditional growth rate of low-knowledge intensity services is higher than the conditional growth rate of high-knowledge intensity services regardless of age. The evidence suggests that the rise of services is driven by the high conditional growth rate of low-knowledge intensity firms and the high survival rate of young high-knowledge intensity firms.

Table 7 The table reports the mean values of services’ age-specific survival and conditional growth rates by knowledge intensity segment based on the 2011Q1–2017Q1 period in NSSO data

1.2 Robustness of the average survival and conditional growth rates

To evaluate the time variation of the average survival and conditional growth rates, we measure the mean values of sector’s age-specific survival and conditional growth rates on subsamples, 2011–2013 vs. 2014–2016. This corresponds to 12 observations for each subsample. Table 8 reports the mean values of sector’s age-specific survival rates by subsample. The survival rate increases with age across all sectors and time periods. While there are some differences across time periods, there is not enough evidence to suggest that these differences are statistically different from zero. Based on point estimates, the survival rate before 2014 was slightly higher than after 2014. However, these differences are not economically significant.

Table 8 The table reports the mean values of sector’s age-specific survival rates computed on subsamples, 2011–2013 vs. 2014–2016, based on the NSSO data

Table 9 reports the mean values of sector’s age-specific conditional growth rates by subsample. The conditional growth rate decreases with age across all sectors and time periods. While there are some differences across time periods, there is not enough evidence to suggest that these differences are statistically different from zero. Based on point estimates, the conditional growth rate before 2014 was lower than after 2014. This could be due to business-cycle fluctuations of the economy during the 2011–2014 period.¹⁰ This is in line with the observation of Pugsley and Şahin (2019). The authors show that the deviations of the mean survival and conditional growth rates from the means are correlated with the business cycle fluctuations. This suggests that taking longer samples is necessary to capture “true” survival and conditional growth rates.

Table 9 The table reports the mean values of sector’s age-specific conditional growth rates computed on subsamples, 2011–2013 vs. 2014–2016, based on the NASSO data

1.3 The survival and conditional growth rates, young versus mature versus old

To understand the heterogeneity in growth dynamics of mature firms (ages 6+), we further segment mature firms. Here, we differentiate between young (ages 1–5), mature (ages 6–10), and old firms (ages 11+). Since we do not have the data on firm age explicitly, we infer firm age implicitly by tracking each for at least 10 years. This way we are able to distinguish between young, mature, and old firms starting from 2013Q1 and onwards. Table 10 reports the mean values of sector’s age-specific survival and conditional growth rates.

Table 10 The table reports the mean values of sector’s age-specific survival and conditional growth rates based on the 2013Q1–2017Q1 period in the NSSO data

The survival rate of firms increases with age, and this pattern holds across sectors on average. The difference in survival rates between young and mature firms is significantly higher than the difference in survival rates between mature and old firms. The conditional growth rate of firms decreases with firm age and this pattern holds across all sectors. Similarly, the difference between young and mature firms is significantly higher than the difference between mature and old firms. This suggests that the differences in growth dynamics of mature and old firms are not very significant compared with the differences in growth dynamics of young and mature firms

This empirical observation is in line with Geurts and Van Biesebroeck (2016), where the authors using the data on Belgian firms show that the post-entry growth dynamics of firms are increasing with firm size until the age of 5. For firms older than 5 years, firm growth follows the Gibrat’s law where size and growth are independent (Lawless 2014)

1.4 The impact of the declining start-up employment—sensitivity analysis

While in the main analysis we compare the linearly increasing start-up employment at 2% a year with the actual start-up employment, here we explore the importance of non-declining start-up employment. To this end, we fit a straight line through the actual start-up employment to estimate the trend and compute the resulting aggregate employment based on the fitted start-up employment. After that, we reverse the declining start-up employment trend and compute the new aggregate employment based on the reversed start-up employment. We explore two scenarios for reversing the trend, constant and increasing start-up employment.

The left panel of Fig. 10 plots the actual start-up employment (the dots) measured in the NSSO data and the counterfactual scenarios for the start-up employment. The straight solid line plots the fitted line based on the point estimates of actual start-up employment over time. The line is downward sloping confirming the secular decline of start-up employment in Belgium. Starting from 2009Q2, we consider two counterfactual scenarios, stable start-up employment (the dashed line) and increasing start-up employment (the dash-dot line). Both of the counterfactual scenarios for start-up employment initiate of the fitted value of start-up employment in 2009Q2.

Fig. 10

The figure plots the evolution of aggregate employment based on the different evolutions of start-up employment. The left panel plots the start-up employment. The dots plot the actual start-up employment observed in the NSSO data. The solid line plots a fitted line over time. Starting from 2009Q2, the dashed and dash-dot lines plot the stable and increasing start-up employment scenarios, respectively. The right panel plots the simulated total employment as a function of start-up employment scenarios

The right panel of Fig. 10 plots the simulated aggregate employment using

$$ {\overset{\sim }{E}}_t=\sum \limits_j\left({s}_t^j{S}_t^{cf}+\sum \limits_{a=1}^5{\overline{x}}_a^j\left(1+{\overline{n}}_a^j\right){\overset{\sim }{E}}_{t-1,a-1}^j+{\overline{x}}_{6+}^j\left(1+{\overline{n}}_{6+}^j\right){\sum}_{a\ge 6}{\overset{\sim }{E}}_{t-1,a-1}^j\right), $$

where ${S}_t^{cf}$ is the start-up employment corresponding to each of the scenarios aforementioned. The dots plot the evolution of simulated aggregate employment based on the actual start-up employment. The solid line plots the evolution of simulated aggregate employment based on the fitted start-up employment. The dashed line plots the evolution of simulated aggregate employment based on the counterfactual stable start-up employment. The dash-dot line plots the evolution of simulated aggregate employment based on the counterfactual increasing start-up employment.

Table 11 reports the percent change of aggregate employment over the 2009Q2–2017Q1 period for different scenarios of start-up employment. For the stable start-up employment case, the aggregate employment is predicted to grow by 1 percentage point more when comparing with the linearly declining start-up employment case. The difference between the actual and fitted cases suggests that even short-term declines in the start-up employment have persistent effect on the aggregate employment.

Table 11 The table reports the percent change of aggregate employment over the 2009Q2–2017Q1 period for different scenarios of start-up employment